the Question 10 refers to the following setting. One concern about the depletion of the ozone layer is increase in UV light will decrease crop yields. An experiment was conducted in a green house where soybean plants were exposed to varying levels of UV levels, measured in Dobson units. At the end of the experiment the yield (kg) vas measured. A regression analysis was performed with the following results: Parameter Estimates Term Intercept uv Estimate 3.9800118 -0.046285 Sid Error t Rato Probolt 0.053774 74.01 <0001 0.010741 hidden 0.0008 10. The least squares regression line is the line that: Upper 95% 4.096 1838 hidden *** the predicted UV values. (b) predicted-yield. (c) predicted UV. Lower 95% 3.8638398 minimizes the sum of the squared differences between the actual UV values and minimizes the sum of the squared residuals between the actual yield and the minimizes the sum the squared differences between the actual yield and the minimizes the sum of the squared residuals between the actual UV reading and the predicted UV reading. (e) minimizes the total variation in the data.
the Question 10 refers to the following setting. One concern about the depletion of the ozone layer is increase in UV light will decrease crop yields. An experiment was conducted in a green house where soybean plants were exposed to varying levels of UV levels, measured in Dobson units. At the end of the experiment the yield (kg) vas measured. A regression analysis was performed with the following results: Parameter Estimates Term Intercept uv Estimate 3.9800118 -0.046285 Sid Error t Rato Probolt 0.053774 74.01 <0001 0.010741 hidden 0.0008 10. The least squares regression line is the line that: Upper 95% 4.096 1838 hidden *** the predicted UV values. (b) predicted-yield. (c) predicted UV. Lower 95% 3.8638398 minimizes the sum of the squared differences between the actual UV values and minimizes the sum of the squared residuals between the actual yield and the minimizes the sum the squared differences between the actual yield and the minimizes the sum of the squared residuals between the actual UV reading and the predicted UV reading. (e) minimizes the total variation in the data.
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
Transcribed Image Text:AP Statistics - Unit 6, Inference for Categorical Data: Propo
Question 10 refers to the following setting. One concern about the depletion of the ozone layer is that the
increase in UV light will decrease crop yields. An experiment was conducted in a green house where
soybean plants were exposed to varying levels of UV levels, measured in Dobson units. At the end of the
experiment the yield (kg) vas measured. A regression analysis was performed with the following results:
Parameter Estimates
Term
Intercept
uv
Sid Error t Ratio Prob>ltl
74.01
-0.046285 0.010741 hidden 0.0008
Estimate
3.9800118
0.053774
<.0001
10. The least squares regression line is the line that:
Lower 95%
3.8638398
the predicted UV values.
(b)
predicted yield.
(c)
predicted UV.
Upper 95%
4.0961838
**** hidden ****
minimizes the sum of the squared differences between the actual UV values and
minimizes the sum of the squared residuals between the actual yield and the
minimizes the sum the squared differences between the actual yield and the
minimizes the sum of the squared residuals between the actual UV reading and
the predicted UV reading.
(e) minimizes the total variation in the data.
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